notebooks/Monkey species classification.ipynb

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              "['config/config.ini']"
            ]
          },
          "execution_count": 1,
          "metadata": {},
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        }
      ],
      "source": [
        "import configparser\n",
        "config = configparser.ConfigParser()\n",
        "config.read('../config/config.ini')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "yFjXoHwUcCXC"
      },
      "outputs": [],
      "source": [
        "import sys\n",
        "sys.path.insert(0, '../source/')\n",
        "from preprocessing_images import PreprocessingImages"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Label</th>\n",
              "      <th>Latin Name</th>\n",
              "      <th>Common Name</th>\n",
              "      <th>Train Images</th>\n",
              "      <th>Validation Images</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>n0</td>\n",
              "      <td>alouatta_palliata\\t</td>\n",
              "      <td>mantled_howler</td>\n",
              "      <td>131</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>n1</td>\n",
              "      <td>erythrocebus_patas\\t</td>\n",
              "      <td>patas_monkey</td>\n",
              "      <td>139</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>n2</td>\n",
              "      <td>cacajao_calvus\\t</td>\n",
              "      <td>bald_uakari</td>\n",
              "      <td>137</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>n3</td>\n",
              "      <td>macaca_fuscata\\t</td>\n",
              "      <td>japanese_macaque</td>\n",
              "      <td>152</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>n4</td>\n",
              "      <td>cebuella_pygmea\\t</td>\n",
              "      <td>pygmy_marmoset</td>\n",
              "      <td>131</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>n5</td>\n",
              "      <td>cebus_capucinus\\t</td>\n",
              "      <td>white_headed_capuchin</td>\n",
              "      <td>141</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>n6</td>\n",
              "      <td>mico_argentatus\\t</td>\n",
              "      <td>silvery_marmoset</td>\n",
              "      <td>132</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>n7</td>\n",
              "      <td>saimiri_sciureus\\t</td>\n",
              "      <td>common_squirrel_monkey</td>\n",
              "      <td>142</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>n8</td>\n",
              "      <td>aotus_nigriceps\\t</td>\n",
              "      <td>black_headed_night_monkey</td>\n",
              "      <td>133</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>n9</td>\n",
              "      <td>trachypithecus_johnii</td>\n",
              "      <td>nilgiri_langur</td>\n",
              "      <td>132</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            "text/plain": [
              "   Label                Latin Name                      Common Name  \\\n",
              "0  n0         alouatta_palliata\\t    mantled_howler                   \n",
              "1  n1        erythrocebus_patas\\t    patas_monkey                     \n",
              "2  n2        cacajao_calvus\\t        bald_uakari                      \n",
              "3  n3        macaca_fuscata\\t        japanese_macaque                 \n",
              "4  n4       cebuella_pygmea\\t        pygmy_marmoset                   \n",
              "5  n5       cebus_capucinus\\t        white_headed_capuchin            \n",
              "6  n6       mico_argentatus\\t        silvery_marmoset                 \n",
              "7  n7      saimiri_sciureus\\t        common_squirrel_monkey           \n",
              "8  n8       aotus_nigriceps\\t        black_headed_night_monkey        \n",
              "9  n9       trachypithecus_johnii    nilgiri_langur                   \n",
              "\n",
              "   Train Images  Validation Images  \n",
              "0           131                 26  \n",
              "1           139                 28  \n",
              "2           137                 27  \n",
              "3           152                 30  \n",
              "4           131                 26  \n",
              "5           141                 28  \n",
              "6           132                 26  \n",
              "7           142                 28  \n",
              "8           133                 27  \n",
              "9           132                 26  "
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import pandas as pd\n",
        "cols = ['Label', 'Latin Name', 'Common Name','Train Images', 'Validation Images']\n",
        "label_df = pd.read_csv(f\"../input/archive/monkey_labels.txt\", names=cols, skiprows=1)\n",
        "label_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WD9i9trvcCXE",
        "outputId": "1333aceb-c5b5-43f9-f8f4-257785355859"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Found 1098 images belonging to 10 classes.\n",
            "Found 272 images belonging to 10 classes.\n"
          ]
        }
      ],
      "source": [
        "train_generator, validation_generator = PreprocessingImages(config).preprocessImages()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EcvAUVo9cCXF",
        "outputId": "faef7742-d9a6-4995-ad7b-e11e9fe23684"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 74, 74, 32)        896       \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 37, 37, 32)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 18, 18, 32)        9248      \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 9, 9, 32)         0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 2592)              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 150)               388950    \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 10)                1510      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 400,604\n",
            "Trainable params: 400,604\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "from cnn_model import CNNModel\n",
        "model = CNNModel(config, train_generator[0][0][0]).build_model()\n",
        "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['acc'])\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "RbnAIz2ScCXG"
      },
      "outputs": [],
      "source": [
        "# Create the callbacks\n",
        "from keras.callbacks import TensorBoard, EarlyStopping\n",
        "callbacks = [\n",
        "            TensorBoard(log_dir=config['CALLBACKS']['TENSORBOARD_LOGDIR'] + \"3\",\n",
        "                        write_images=config['CALLBACKS']['TENSORBOARD_WRITEIMAGES'],\n",
        "                        write_graph=config['CALLBACKS']['TENSORBOARD_WRITEGRAPH'],\n",
        "                        update_freq=config['CALLBACKS']['TENSORBOARD_UPDATEFREQ']\n",
        "                        ),\n",
        "            EarlyStopping(monitor=config['CALLBACKS']['EARLYSTOPPING_MONITOR'],\n",
        "                          mode=config['CALLBACKS']['EARLYSTOPPING_MODE'],\n",
        "                          patience=int(config['CALLBACKS']['EARLYSTOPPING_PATIENCE']),\n",
        "                          verbose=1)\n",
        "]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ccjsp9l-cCXH",
        "outputId": "75956a7d-fb46-4c8d-c19a-328696ae203d"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-7-42bba9ec74c2>:1: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
            "  history = model.fit_generator(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/200\n",
            "17/17 [==============================] - 48s 2s/step - loss: 2.2951 - acc: 0.1518 - val_loss: 2.2217 - val_acc: 0.1680\n",
            "Epoch 2/200\n",
            "17/17 [==============================] - 39s 2s/step - loss: 2.1459 - acc: 0.2157 - val_loss: 2.0311 - val_acc: 0.2500\n",
            "Epoch 3/200\n",
            "17/17 [==============================] - 51s 3s/step - loss: 2.0291 - acc: 0.2640 - val_loss: 1.9767 - val_acc: 0.3008\n",
            "Epoch 4/200\n",
            "17/17 [==============================] - 42s 3s/step - loss: 1.8786 - acc: 0.3443 - val_loss: 1.8628 - val_acc: 0.3594\n",
            "Epoch 5/200\n",
            "17/17 [==============================] - 41s 3s/step - loss: 1.7466 - acc: 0.3772 - val_loss: 1.8142 - val_acc: 0.3242\n",
            "Epoch 6/200\n",
            "17/17 [==============================] - 41s 2s/step - loss: 1.7175 - acc: 0.4052 - val_loss: 1.5751 - val_acc: 0.4805\n",
            "Epoch 7/200\n",
            "17/17 [==============================] - 40s 2s/step - loss: 1.6158 - acc: 0.4294 - val_loss: 1.6549 - val_acc: 0.4414\n",
            "Epoch 8/200\n",
            "17/17 [==============================] - 40s 2s/step - loss: 1.6130 - acc: 0.4255 - val_loss: 1.4316 - val_acc: 0.5156\n",
            "Epoch 9/200\n",
            "17/17 [==============================] - 41s 2s/step - loss: 1.5205 - acc: 0.4603 - val_loss: 1.3828 - val_acc: 0.5508\n",
            "Epoch 10/200\n",
            "17/17 [==============================] - 41s 2s/step - loss: 1.4772 - acc: 0.4874 - val_loss: 1.3271 - val_acc: 0.5391\n",
            "Epoch 11/200\n",
            "17/17 [==============================] - 40s 2s/step - loss: 1.4473 - acc: 0.4797 - val_loss: 1.2974 - val_acc: 0.5820\n",
            "Epoch 12/200\n",
            "17/17 [==============================] - 42s 3s/step - loss: 1.4354 - acc: 0.4990 - val_loss: 1.2773 - val_acc: 0.5781\n",
            "Epoch 13/200\n",
            "17/17 [==============================] - 40s 2s/step - loss: 1.3913 - acc: 0.5068 - val_loss: 1.2528 - val_acc: 0.5742\n",
            "Epoch 14/200\n",
            "17/17 [==============================] - 42s 3s/step - loss: 1.3659 - acc: 0.5126 - val_loss: 1.2680 - val_acc: 0.5430\n",
            "Epoch 15/200\n",
            "17/17 [==============================] - 40s 2s/step - loss: 1.3387 - acc: 0.5542 - val_loss: 1.2307 - val_acc: 0.5781\n",
            "Epoch 16/200\n",
            "17/17 [==============================] - 42s 3s/step - loss: 1.2936 - acc: 0.5368 - val_loss: 1.3631 - val_acc: 0.5156\n",
            "Epoch 17/200\n",
            "17/17 [==============================] - 39s 2s/step - loss: 1.2803 - acc: 0.5522 - val_loss: 1.1618 - val_acc: 0.5977\n",
            "Epoch 18/200\n",
            "17/17 [==============================] - 42s 2s/step - loss: 1.2354 - acc: 0.5735 - val_loss: 1.1464 - val_acc: 0.5977\n",
            "Epoch 19/200\n",
            "17/17 [==============================] - 42s 2s/step - loss: 1.2535 - acc: 0.5532 - val_loss: 1.0794 - val_acc: 0.6133\n",
            "Epoch 20/200\n",
            "17/17 [==============================] - 41s 2s/step - loss: 1.2065 - acc: 0.5725 - val_loss: 1.2250 - val_acc: 0.5781\n",
            "Epoch 21/200\n",
            "17/17 [==============================] - 41s 2s/step - loss: 1.2215 - acc: 0.5580 - val_loss: 1.0950 - val_acc: 0.6484\n",
            "Epoch 22/200\n",
            "17/17 [==============================] - 41s 2s/step - loss: 1.2113 - acc: 0.5687 - val_loss: 1.0947 - val_acc: 0.6211\n",
            "Epoch 23/200\n",
            "17/17 [==============================] - 42s 2s/step - loss: 1.2029 - acc: 0.5754 - val_loss: 1.1097 - val_acc: 0.6172\n",
            "Epoch 24/200\n",
            "17/17 [==============================] - 52s 3s/step - loss: 1.1453 - acc: 0.6112 - val_loss: 1.1150 - val_acc: 0.5781\n",
            "Epoch 24: early stopping\n"
          ]
        }
      ],
      "source": [
        "history = model.fit_generator(\n",
        "    train_generator,\n",
        "    steps_per_epoch=train_generator.samples // train_generator.batch_size,\n",
        "    epochs = int(config['CNN_CONFIGURATION']['EPOCHS']),\n",
        "    validation_data=validation_generator,\n",
        "    validation_steps=validation_generator.samples // validation_generator.batch_size,\n",
        "    callbacks=callbacks,\n",
        "    verbose = 1\n",
        ")"
      ]
    },
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        "outputId": "08450a7c-5baa-4fe1-ed19-83e242cbdd9f"
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from aux_functions import pltResults\n",
        "# Plot accuracy graph\n",
        "pltResults(\n",
        "    '../logs/',\n",
        "    history.history['acc'],\n",
        "    history.history['val_acc'],\n",
        "    'Model accuracy',\n",
        "    'epoch',\n",
        "    'accuracy')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "dIP2otLtcCXK",
        "outputId": "cce276dd-45ec-438e-e335-7f1f7d4f3096"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plot loss graph\n",
        "pltResults(\n",
        "    '../logs/',\n",
        "    history.history['loss'],\n",
        "    history.history['val_loss'],\n",
        "    'Model loss',\n",
        "    'epoch',\n",
        "    'loss')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KJrPoHZeOfUr"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "env",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.6"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}